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<h1 class="title toc-ignore">Repsc vignette</h1>
<h4 class="author">David Brocks</h4>
<h4 class="date">2019-08-20</h4>



<div id="workflow-human-10x-scrna-seq-dataset-5" class="section level1">
<h1>Workflow human 10x scRNA-seq dataset (5’)</h1>
<p>In this tutorial, we are going to utilize 5’ scRNA-seq data on epigenetically de-repressed cancer cell lines to quantify transposable element (TE) expression levels at single-cell and locus resolution. Following the workflow, you’ll learn the specifics of Repsc to adapt it to your single-cell dataset.</p>
<div id="getting-started" class="section level2">
<h2>Getting started</h2>
<p>We start the workflow by loading Repsc and the human hg38 <em>BSgenome</em> object into our R environment:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">devtools<span class="op">::</span><span class="kw">load_all</span>(<span class="st">'/net/mraid14/export/tgdata/users/davidbr/src/Repsc/'</span>)
<span class="co">#&gt; Loading Repsc</span>
devtools<span class="op">::</span><span class="kw">load_all</span>(<span class="st">'/net/mraid14/export/tgdata/users/davidbr/src/Reputils/'</span>)
<span class="co">#&gt; Loading Reputils</span>
devtools<span class="op">::</span><span class="kw">load_all</span>(<span class="st">'/net/mraid14/export/tgdata/users/davidbr/src/Repdata/'</span>)
<span class="co">#&gt; Loading Repdata</span>

<span class="co"># adjust to your genome of interest (e.g. BSgenome.Mmusculus.UCSC.mm10)</span>
<span class="kw">library</span>(BSgenome.Hsapiens.UCSC.hg38)
<span class="co">#&gt; Loading required package: BSgenome</span>
<span class="co">#&gt; Loading required package: BiocGenerics</span>
<span class="co">#&gt; Loading required package: parallel</span>
<span class="co">#&gt; </span>
<span class="co">#&gt; Attaching package: 'BiocGenerics'</span>
<span class="co">#&gt; The following objects are masked from 'package:parallel':</span>
<span class="co">#&gt; </span>
<span class="co">#&gt;     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB</span>
<span class="co">#&gt; The following objects are masked from 'package:Reputils':</span>
<span class="co">#&gt; </span>
<span class="co">#&gt;     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, combine, intersect, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply,</span>
<span class="co">#&gt;     parSapplyLB, setdiff, union, which</span>
<span class="co">#&gt; The following objects are masked from 'package:Repsc':</span>
<span class="co">#&gt; </span>
<span class="co">#&gt;     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, combine, counts, intersect, parApply, parCapply, parLapply, parLapplyLB, parRapply,</span>
<span class="co">#&gt;     parSapply, parSapplyLB, setdiff, union, which</span>
<span class="co">#&gt; The following objects are masked from 'package:stats':</span>
<span class="co">#&gt; </span>
<span class="co">#&gt;     IQR, mad, sd, var, xtabs</span>
<span class="co">#&gt; The following objects are masked from 'package:base':</span>
<span class="co">#&gt; </span>
<span class="co">#&gt;     anyDuplicated, append, as.data.frame, basename, cbind, colMeans, colnames, colSums, dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect,</span>
<span class="co">#&gt;     is.unsorted, lapply, lengths, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rowMeans, rownames, rowSums, sapply,</span>
<span class="co">#&gt;     setdiff, sort, table, tapply, union, unique, unsplit, which, which.max, which.min</span>
<span class="co">#&gt; Loading required package: S4Vectors</span>
<span class="co">#&gt; Loading required package: stats4</span>
<span class="co">#&gt; </span>
<span class="co">#&gt; Attaching package: 'S4Vectors'</span>
<span class="co">#&gt; The following objects are masked from 'package:Reputils':</span>
<span class="co">#&gt; </span>
<span class="co">#&gt;     first, rename, second, values</span>
<span class="co">#&gt; The following objects are masked from 'package:Repsc':</span>
<span class="co">#&gt; </span>
<span class="co">#&gt;     first, rename, second, values</span>
<span class="co">#&gt; The following object is masked from 'package:base':</span>
<span class="co">#&gt; </span>
<span class="co">#&gt;     expand.grid</span>
<span class="co">#&gt; Loading required package: IRanges</span>
<span class="co">#&gt; </span>
<span class="co">#&gt; Attaching package: 'IRanges'</span>
<span class="co">#&gt; The following objects are masked from 'package:Reputils':</span>
<span class="co">#&gt; </span>
<span class="co">#&gt;     collapse, desc, shift, slice, trim</span>
<span class="co">#&gt; The following objects are masked from 'package:Repsc':</span>
<span class="co">#&gt; </span>
<span class="co">#&gt;     collapse, desc, shift, slice, trim</span>
<span class="co">#&gt; Loading required package: GenomeInfoDb</span>
<span class="co">#&gt; Loading required package: GenomicRanges</span>
<span class="co">#&gt; Loading required package: Biostrings</span>
<span class="co">#&gt; Loading required package: XVector</span>
<span class="co">#&gt; </span>
<span class="co">#&gt; Attaching package: 'Biostrings'</span>
<span class="co">#&gt; The following object is masked from 'package:base':</span>
<span class="co">#&gt; </span>
<span class="co">#&gt;     strsplit</span>
<span class="co">#&gt; Loading required package: rtracklayer</span>
<span class="co">#&gt; </span>
<span class="co">#&gt; Attaching package: 'rtracklayer'</span>
<span class="co">#&gt; The following object is masked from 'package:Repsc':</span>
<span class="co">#&gt; </span>
<span class="co">#&gt;     export</span></code></pre></div>
</div>
<div id="create-scset" class="section level2">
<h2>Create scSet</h2>
<p>We then import our gene and TE annotation files as <a href="https://bioconductor.org/packages/release/bioc/vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.html">GRanges objects</a> followed by Repsc-specific curation and formatting using the <code>curateGenes</code> and <code>curateTEs</code> functions.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># path to Gencode gtf file (provided)</span>
gene_path &lt;-<span class="st"> '~/davidbr/tools/cellranger/references/refdata-cellranger-GRCh38-1.2.0/genes/genes.gtf'</span>

<span class="co"># path to RepeatMasker hg38 repeat annotation (provided)</span>
rmsk_path &lt;-<span class="st"> </span><span class="kw">system.file</span>(<span class="dt">package =</span> <span class="st">'Repdata'</span>, 
                         <span class="st">'extdata'</span>, 
                         <span class="st">'hg38.fa.out.gz'</span>)
                         
<span class="co"># creating the scSet</span>
sc &lt;-<span class="st"> </span><span class="kw">createScSet</span>(<span class="dt">genome   =</span> Hsapiens,
                  <span class="dt">protocol =</span> <span class="st">'fiveprime'</span>,
                  <span class="dt">tes      =</span> rmsk_path,
                  <span class="dt">genes    =</span> gene_path)
<span class="co">#&gt; Curating gene intervals</span>
<span class="co">#&gt; Curating TE intervals</span>
<span class="co">#&gt; Resolving overlaps</span>
<span class="co">#&gt; Labeling modified intervals</span>
<span class="co">#&gt; Labeling gene overlaps</span>
<span class="co">#&gt; Created new scSet at Tue Aug 20 14:04:25 2019</span></code></pre></div>
</div>
<div id="compute-multiple-sequence-alignments" class="section level2">
<h2>Compute multiple sequence alignments</h2>
<p>Repsc computes the read/UMI coverage along genes and the consensus model of TE families. This can be useful to sanity check 5’/3’ enrichment (depending on the protocol) and to identify putative TE consensus TSSs (5’ protocols), polyA-sites (3’ protocols), and to distinguish true de-repression from spurious background signal (e.g. intronic TE read mis-assignment, broad-scale genomic background transcription, etc.). As a rough estimate, we can utilize the consensus mapping information from Repeatmasker or DFAM output files for that purpose. This will usually provide reasonable results for highly conserved families. To increase accuracy, Repsc can also compute family-wise multiple sequence alignments to improve mapping of individual loci onto a de novo alignment. When time and computational ressources are no limitation, we recommend this step by running:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># path to bam files containing mapped reads                         </span>
bam_paths &lt;-<span class="st">  </span><span class="kw">c</span>(
                <span class="kw">dir</span>(<span class="st">'/net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dmso/aligned/'</span>, 
                  <span class="dt">pattern =</span> <span class="st">'_deduplicated_chr[0-9, X, Y, MT]+.bam$'</span>, 
                  <span class="dt">recursive =</span> <span class="ot">TRUE</span>,
                  <span class="dt">full.names =</span> <span class="ot">TRUE</span>),
                <span class="kw">dir</span>(<span class="st">'/net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dacsb/aligned/'</span>, 
                  <span class="dt">pattern =</span> <span class="st">'_deduplicated_chr[0-9, X, Y, MT]+.bam$'</span>, 
                  <span class="dt">recursive =</span> <span class="ot">TRUE</span>,
                  <span class="dt">full.names =</span> <span class="ot">TRUE</span>),
                <span class="kw">dir</span>(<span class="st">'/net/mraid14/export/data/users/davidbr/proj/epitherapy/data/h1299/10x/dmso/aligned/'</span>, 
                  <span class="dt">pattern =</span> <span class="st">'_deduplicated_chr[0-9, X, Y, MT]+.bam$'</span>, 
                  <span class="dt">recursive =</span> <span class="ot">TRUE</span>,
                  <span class="dt">full.names =</span> <span class="ot">TRUE</span>),
                <span class="kw">dir</span>(<span class="st">'/net/mraid14/export/data/users/davidbr/proj/epitherapy/data/h1299/10x/dacsb/aligned/'</span>, 
                  <span class="dt">pattern =</span> <span class="st">'_deduplicated_chr[0-9, X, Y, MT]+.bam$'</span>, 
                  <span class="dt">recursive =</span> <span class="ot">TRUE</span>,
                  <span class="dt">full.names =</span> <span class="ot">TRUE</span>)
                )

hdf5_paths &lt;-<span class="st"> </span><span class="kw">c</span>(
                <span class="st">'/net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dmso/hct116_DMSO/outs/filtered_gene_bc_matrices_h5.h5'</span>,
                <span class="st">'/net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dacsb/hct116_DACSB/outs/filtered_gene_bc_matrices_h5.h5'</span>,
                <span class="st">'/net/mraid14/export/data/users/davidbr/proj/epitherapy/data/h1299/10x/dmso/h1299_DMSO/outs/filtered_gene_bc_matrices_h5.h5'</span>,
                <span class="st">'/net/mraid14/export/data/users/davidbr/proj/epitherapy/data/h1299/10x/dacsb/h1299_DACSB/outs/filtered_gene_bc_matrices_h5.h5'</span>
                )             

<span class="co"># create a data.frame specifying import parameters                 </span>
input_df    &lt;-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">paths   =</span> bam_paths,
                        <span class="dt">paired  =</span> <span class="ot">TRUE</span>,       <span class="co"># use FALSE for single-end libraries</span>
                        <span class="dt">mate    =</span> <span class="st">'first'</span>,    <span class="co"># only imports the first mate of properly aligned read pairs, set to NA when using single-end libraries</span>
                        <span class="dt">barcode =</span> <span class="st">'CB'</span>,       <span class="co"># 10x barcode included in BAM flag</span>
                        <span class="dt">chunk   =</span> <span class="kw">ntile</span>(bam_paths, <span class="dv">25</span>),
                        <span class="dt">hdf5    =</span> <span class="kw">rep</span>(hdf5_paths, <span class="dt">each =</span> <span class="dv">25</span>),
                        <span class="dt">meta    =</span> <span class="kw">rep</span>(<span class="kw">c</span>(<span class="st">'hct116_dmso'</span>, <span class="st">'hct116_dacsb'</span>, <span class="st">'h1299_dmso'</span>, <span class="st">'h1299_dacsb'</span>), <span class="dt">each =</span> <span class="dv">25</span>),
                        <span class="dt">stringsAsFactors =</span> <span class="ot">FALSE</span>)
                        
<span class="kw">head</span>(input_df)                      
<span class="co">#&gt;                                                                                                                                paths paired  mate barcode chunk</span>
<span class="co">#&gt; 1  /net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dmso/aligned//chunked_bams/possorted_deduplicated_chr1.bam   TRUE first      CB    19</span>
<span class="co">#&gt; 2 /net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dmso/aligned//chunked_bams/possorted_deduplicated_chr10.bam   TRUE first      CB    20</span>
<span class="co">#&gt; 3 /net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dmso/aligned//chunked_bams/possorted_deduplicated_chr11.bam   TRUE first      CB    20</span>
<span class="co">#&gt; 4 /net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dmso/aligned//chunked_bams/possorted_deduplicated_chr12.bam   TRUE first      CB    20</span>
<span class="co">#&gt; 5 /net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dmso/aligned//chunked_bams/possorted_deduplicated_chr13.bam   TRUE first      CB    20</span>
<span class="co">#&gt; 6 /net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dmso/aligned//chunked_bams/possorted_deduplicated_chr14.bam   TRUE first      CB    21</span>
<span class="co">#&gt;                                                                                                                           hdf5        meta</span>
<span class="co">#&gt; 1 /net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dmso/hct116_DMSO/outs/filtered_gene_bc_matrices_h5.h5 hct116_dmso</span>
<span class="co">#&gt; 2 /net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dmso/hct116_DMSO/outs/filtered_gene_bc_matrices_h5.h5 hct116_dmso</span>
<span class="co">#&gt; 3 /net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dmso/hct116_DMSO/outs/filtered_gene_bc_matrices_h5.h5 hct116_dmso</span>
<span class="co">#&gt; 4 /net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dmso/hct116_DMSO/outs/filtered_gene_bc_matrices_h5.h5 hct116_dmso</span>
<span class="co">#&gt; 5 /net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dmso/hct116_DMSO/outs/filtered_gene_bc_matrices_h5.h5 hct116_dmso</span>
<span class="co">#&gt; 6 /net/mraid14/export/data/users/davidbr/proj/epitherapy/data/hct116/10x/dmso/hct116_DMSO/outs/filtered_gene_bc_matrices_h5.h5 hct116_dmso</span></code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">sc &lt;-<span class="st"> </span><span class="kw">addCounts</span>(sc,
                <span class="dt">bams     =</span> input_df,
                <span class="dt">bin_size =</span> <span class="dv">25</span>,
                <span class="dt">msa_dir  =</span> <span class="ot">NULL</span>,
                <span class="dt">use_gcluster =</span> <span class="ot">TRUE</span>)
<span class="co">#&gt; Preparing for distribution...</span>
<span class="co">#&gt; bamsgenestestes_3p</span>
<span class="co">#&gt; Running the commands...</span>
<span class="co">#&gt; 0%............................8%...12%...16%...20%...24%...32%...40%....56%...64%...72%....76%.....80%...92%....96%.....100%</span>
<span class="co">#&gt; Importing 10x gene counts from hdf5 file(s)</span>
<span class="co">#&gt; Computing genomic CPN of TE bins</span>
<span class="co">#&gt; Computing ribosomal/mitochondrial percentage per cell</span></code></pre></div>
</div>
<div id="call-cells" class="section level2">
<h2>Call cells</h2>
<p>To distinguish real cells from empty droplets, we utilize the <code>emptyDrops</code> function from the <a href="https://rdrr.io/github/MarioniLab/DropletUtils/">DropletUtils</a> package[1].</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plotCells</span>(sc)
<span class="co">#&gt; Picking joint bandwidth of 865</span>
<span class="co">#&gt; Picking joint bandwidth of 0.455</span></code></pre></div>
<p><img src="" style="display: block; margin: auto;" /></p>
</div>
<div id="mapping" class="section level2">
<h2>Mapping</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plotMapping</span>(sc)</code></pre></div>
<p><img src="" style="display: block; margin: auto;" /></p>
</div>
</div>
<div id="feature-selection" class="section level1">
<h1>Feature selection</h1>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">sc &lt;-<span class="st"> </span><span class="kw">selectFeatures</span>(sc)
<span class="co">#&gt; Downsampling counts to 3690</span>
<span class="co">#&gt; Compating variance over mean</span>
<span class="co">#&gt; Running Louvain community detection</span>
<span class="co">#&gt; 9 communities found</span>
<span class="kw">plotFeatures</span>(sc)
<span class="co">#&gt; Joining, by = &quot;name&quot;</span>
<span class="co">#&gt; Joining, by = &quot;module&quot;</span></code></pre></div>
<p><img src="" style="display: block; margin: auto;" /></p>
</div>
<div id="call-peaks" class="section level1">
<h1>Call peaks</h1>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">sc &lt;-<span class="st"> </span><span class="kw">selectPeaks</span>(sc)
<span class="co">#&gt; Calling peaks</span>
<span class="kw">plotPeaks</span>(sc)</code></pre></div>
<p><img src="" style="display: block; margin: auto;" /></p>
</div>
<div id="references" class="section level1">
<h1>References</h1>
<p>[1]</p>
</div>
<div id="session-information" class="section level1">
<h1>Session information</h1>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">sessionInfo</span>()
<span class="co">#&gt; R version 3.5.3 (2019-03-11)</span>
<span class="co">#&gt; Platform: x86_64-pc-linux-gnu (64-bit)</span>
<span class="co">#&gt; Running under: CentOS Linux 7 (Core)</span>
<span class="co">#&gt; </span>
<span class="co">#&gt; Matrix products: default</span>
<span class="co">#&gt; BLAS/LAPACK: /net/mraid14/export/data/users/eladch/tools/CO7/mkl/2018.3/compilers_and_libraries_2018.3.222/linux/mkl/lib/intel64_lin/libmkl_gf_lp64.so</span>
<span class="co">#&gt; </span>
<span class="co">#&gt; locale:</span>
<span class="co">#&gt;  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8      </span>
<span class="co">#&gt;  [8] LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       </span>
<span class="co">#&gt; </span>
<span class="co">#&gt; attached base packages:</span>
<span class="co">#&gt; [1] stats4    parallel  stats     graphics  grDevices datasets  utils     methods   base     </span>
<span class="co">#&gt; </span>
<span class="co">#&gt; other attached packages:</span>
<span class="co">#&gt;  [1] hexbin_1.27.2                     BSgenome.Hsapiens.UCSC.hg38_1.4.1 BSgenome_1.50.0                   rtracklayer_1.42.2                Biostrings_2.50.2                </span>
<span class="co">#&gt;  [6] XVector_0.22.0                    GenomicRanges_1.34.0              GenomeInfoDb_1.18.2               IRanges_2.16.0                    S4Vectors_0.20.1                 </span>
<span class="co">#&gt; [11] BiocGenerics_0.28.0               Repdata_0.0.0.9000                Reputils_0.0.0.9000               Repsc_0.0.0.9000                  tgstat_2.3.2                     </span>
<span class="co">#&gt; [16] misha_4.0.4                      </span>
<span class="co">#&gt; </span>
<span class="co">#&gt; loaded via a namespace (and not attached):</span>
<span class="co">#&gt;   [1] colorspace_1.4-1            rjson_0.2.20                ellipsis_0.1.0              ggridges_0.5.1              rprojroot_1.3-2             GlobalOptions_0.1.0        </span>
<span class="co">#&gt;   [7] fs_1.2.7                    rstudioapi_0.9.0            listenv_0.7.0               remotes_2.0.2               ggrepel_0.8.0               ggfittext_0.8.1            </span>
<span class="co">#&gt;  [13] bit64_0.9-7                 codetools_0.2-16            R.methodsS3_1.7.1           phylogram_2.1.0             knitr_1.22                  pkgload_1.0.2              </span>
<span class="co">#&gt;  [19] Rsamtools_1.34.1            doMC_1.3.5                  R.oo_1.22.0                 compiler_3.5.3              backports_1.1.4             assertthat_0.2.1           </span>
<span class="co">#&gt;  [25] Matrix_1.2-16               lazyeval_0.2.2              cli_1.1.0                   treemapify_2.5.3            htmltools_0.3.6             prettyunits_1.0.2          </span>
<span class="co">#&gt;  [31] tools_3.5.3                 igraph_1.2.4                gtable_0.3.0                glue_1.3.1                  GenomeInfoDbData_1.2.0      reshape2_1.4.3             </span>
<span class="co">#&gt;  [37] dplyr_0.8.0.1               Rcpp_1.0.1                  Biobase_2.42.0              ape_5.3                     nlme_3.1-137                DECIPHER_2.10.2            </span>
<span class="co">#&gt;  [43] iterators_1.0.10            xfun_0.5                    stringr_1.4.0               globals_0.12.4              plyranges_1.2.0             ps_1.3.0                   </span>
<span class="co">#&gt;  [49] testthat_2.0.1              devtools_2.0.1              XML_3.98-1.19               future_1.13.0               zoo_1.8-4                   zlibbioc_1.28.0            </span>
<span class="co">#&gt;  [55] scales_1.0.0                SummarizedExperiment_1.12.0 RColorBrewer_1.1-2          yaml_2.2.0                  memoise_1.1.0               gridExtra_2.3              </span>
<span class="co">#&gt;  [61] ggplot2_3.1.0               stringi_1.4.3               RSQLite_2.1.1               desc_1.2.0                  foreach_1.4.4               kmer_1.1.1                 </span>
<span class="co">#&gt;  [67] pkgbuild_1.0.3              BiocParallel_1.16.6         rlang_0.3.2                 pkgconfig_2.0.2             matrixStats_0.54.0          bitops_1.0-6               </span>
<span class="co">#&gt;  [73] evaluate_0.13               lattice_0.20-38             purrr_0.3.2                 labeling_0.3                GenomicAlignments_1.18.1    cowplot_0.9.4              </span>
<span class="co">#&gt;  [79] bit_1.1-14                  processx_3.3.0              tidyselect_0.2.5            plyr_1.8.4                  magrittr_1.5                R6_2.4.0                   </span>
<span class="co">#&gt;  [85] DelayedArray_0.8.0          DBI_1.0.0                   pillar_1.3.1                withr_2.1.2                 RCurl_1.95-4.12             tibble_2.1.1               </span>
<span class="co">#&gt;  [91] crayon_1.3.4                hdf5r_1.2.0                 doFuture_0.8.0              rmarkdown_1.12              GetoptLong_0.1.7            usethis_1.4.0              </span>
<span class="co">#&gt;  [97] grid_3.5.3                  data.table_1.12.2           blob_1.1.1                  FNN_1.1.3                   callr_3.2.0                 forcats_0.4.0              </span>
<span class="co">#&gt; [103] digest_0.6.19               tidyr_0.8.3                 R.utils_2.8.0               munsell_0.5.0               fst_0.9.0                   viridisLite_0.3.0          </span>
<span class="co">#&gt; [109] sessioninfo_1.1.1</span></code></pre></div>
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